ConceptM$^3$oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology
The article introduces ConceptM$^3$oE, a new framework for computational pathology that integrates multimodal diagnostic inputs. This approach enhances interpretability and performance in medical AI by utilizing a mixture-of-experts architecture. The framework has shown improved performance in data-limited scenarios, making it a promising tool for clinical decision-making.
- ▪ConceptM$^3$oE embeds concept formation within interaction-aware mixture-of-experts pathways.
- ▪The architecture decomposes evidence into modality-specific experts, allowing for better diagnostic clarity.
- ▪It improves limited-data performance, increasing macro-F1 scores significantly compared to non-concept-informed baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24399 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.24399 (cs) [Submitted on 23 May 2026] Title:ConceptM$^3$oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology Authors:Xuan Wang, Zhongling Xu, Gopi Kannedhara, Joakim Nguyen, Jian Yu, Jinrui Fang, Abdurrahmaan Baghdadi, Tianlong Chen, Awais Naeem, Chandra Krishnan, Edward Castillo, Andrew H. Song, Ankita Shukla, Ying Ding, Nicholas Konz, Hairong Wang View a PDF of the paper titled ConceptM$^3$oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology, by Xuan Wang and 15 other authors View PDF HTML (experimental) Abstract:Healthcare models are transitioning from unimodal prediction toward multimodal reasoning over heterogeneous diagnostic inputs.
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